DiPhon: Diffusion on Graphons for Scalable Graph Generation

• URL: http://arxiv.org/abs/2607.07232v1
• Date: Wed, 08 Jul 2026 10:15:17 GMT
• Title: DiPhon: Diffusion on Graphons for Scalable Graph Generation
• Abstract summary: Diffusion models represent a leading paradigm for graph generation, with notable impact in domains such as molecular design.<n>We propose DiPhon, a discretized graph-level process that mimics these dynamics on finite graphs.<n>We prove that DiPhon matches exactly the first moment of the marginal distributions induced by the continuous graphon process, and approximates the second moment up to a closed-form discrepancy.
• Score: 100.75398811422308
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diffusion models represent a leading paradigm for graph generation, with notable impact in domains such as molecular design. Yet, scaling these models to large graphs remains an open problem. We approach this question in the dense-graph setting through the lens of graphons, the size-agnostic limit objects of dense graph sequences, to study how structural graph statistics behave across node-size scales. This perspective leads to DiPhon, a diffusion framework for size-scalable graph generation. Specifically, we formulate a continuous diffusion process on the graphon space via a Jacobi stochastic differential equation (SDE), and propose DiPhon, a discretized graph-level process that mimics these dynamics on finite graphs. We further derive the corresponding reverse-time process, which requires access to the marginal score. For the Jacobi process, this score interestingly admits a tractable form, which we estimate from data via graph denoising and plug into the reverse process to generate graph samples. We prove that DiPhon matches exactly the first moment of the marginal distributions induced by the continuous graphon process, and approximates the second moment up to a closed-form discrepancy. Thus, DiPhon inherits key size-agnostic statistical properties of the graphon dynamics, providing a principled route toward scalable graph generation. Empirically, we demonstrate this scalability by training on small graphs and generating progressively larger graphs at inference time, without retraining, while preserving their core topological properties.

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